



import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_		 =					logging.get_logger(__name__)
UpperCAmelCase_		 =					{"""vocab_file""": """spiece.model"""}

UpperCAmelCase_		 =					{
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    }
}

UpperCAmelCase_		 =					{
    """albert-base-v1""": 5_1_2,
    """albert-large-v1""": 5_1_2,
    """albert-xlarge-v1""": 5_1_2,
    """albert-xxlarge-v1""": 5_1_2,
    """albert-base-v2""": 5_1_2,
    """albert-large-v2""": 5_1_2,
    """albert-xlarge-v2""": 5_1_2,
    """albert-xxlarge-v2""": 5_1_2,
}

UpperCAmelCase_		 =					"""▁"""


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: Tuple =  VOCAB_FILES_NAMES
 a__					: str =  PRETRAINED_VOCAB_FILES_MAP
 a__					: int =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

 def __init__(    self					:					Dict			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					Optional[int]=True			,						__lowerCAmelCase					:					Tuple=True			,						__lowerCAmelCase					:					Optional[Any]=False			,						__lowerCAmelCase					:					Optional[Any]="[CLS]"			,						__lowerCAmelCase					:					List[Any]="[SEP]"			,						__lowerCAmelCase					:					List[str]="<unk>"			,						__lowerCAmelCase					:					str="[SEP]"			,						__lowerCAmelCase					:					Optional[Any]="<pad>"			,						__lowerCAmelCase					:					int="[CLS]"			,						__lowerCAmelCase					:					List[str]="[MASK]"			,						__lowerCAmelCase					:					Optional[Dict[str, Any]] = None			,						**__lowerCAmelCase					:					List[Any]			,						)   ->	None:
    # Mask token behave like a normal word, i.e. include the space before it and
    # is included in the raw text, there should be a match in a non-normalized sentence.
    _A      =       (
        AddedToken(__lowerCAmelCase			,						lstrip=__lowerCAmelCase			,						rstrip=__lowerCAmelCase			,						normalized=__lowerCAmelCase      )
        if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      )
        else mask_token
    )

    _A      =       {} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        do_lower_case=__lowerCAmelCase			,						remove_space=__lowerCAmelCase			,						keep_accents=__lowerCAmelCase			,						bos_token=__lowerCAmelCase			,						eos_token=__lowerCAmelCase			,						unk_token=__lowerCAmelCase			,						sep_token=__lowerCAmelCase			,						pad_token=__lowerCAmelCase			,						cls_token=__lowerCAmelCase			,						mask_token=__lowerCAmelCase			,						sp_model_kwargs=self.sp_model_kwargs			,						**__lowerCAmelCase			,						)

    _A      =       do_lower_case
    _A      =       remove_space
    _A      =       keep_accents
    _A      =       vocab_file

    _A      =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.Load(__lowerCAmelCase      )

 @property
 def     snake_case_      (    self					:					List[Any]      )   ->	int:
    return len(self.sp_model      )

 def     snake_case_      (    self					:					Optional[int]      )   ->	List[str]:
    _A      =       {self.convert_ids_to_tokens(__lowerCAmelCase      ): i for i in range(self.vocab_size      )}
    vocab.update(self.added_tokens_encoder      )
    return vocab

 def __getstate__(    self					:					str      )   ->	str:
    _A      =       self.__dict__.copy()
    _A      =       None
    return state

 def __setstate__(    self					:					Dict			,						__lowerCAmelCase					:					Dict      )   ->	Tuple:
    _A      =       d

    # for backward compatibility
    if not hasattr(self			,						'''sp_model_kwargs'''      ):
       _A      =       {}

    _A      =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
    self.sp_model.Load(self.vocab_file      )

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					str      )   ->	Any:
    if self.remove_space:
       _A      =       ''' '''.join(inputs.strip().split()      )
    else:
       _A      =       inputs
    _A      =       outputs.replace('''``'''			,						'''"'''      ).replace('''\'\''''			,						'''"'''      )

    if not self.keep_accents:
       _A      =       unicodedata.normalize('''NFKD'''			,						__lowerCAmelCase      )
       _A      =       ''''''.join([c for c in outputs if not unicodedata.combining(__lowerCAmelCase      )]      )
    if self.do_lower_case:
       _A      =       outputs.lower()

    return outputs

 def     snake_case_      (    self					:					Union[str, Any]			,						__lowerCAmelCase					:					str      )   ->	List[str]:
    _A      =       self.preprocess_text(__lowerCAmelCase      )
    _A      =       self.sp_model.encode(__lowerCAmelCase			,						out_type=__lowerCAmelCase      )
    _A      =       []
    for piece in pieces:
       if len(__lowerCAmelCase      ) > 1 and piece[-1] == str(''','''      ) and piece[-2].isdigit():
          _A      =       self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCAmelCase			,						''''''      )      )
          if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
             if len(cur_pieces[0]      ) == 1:
                _A      =       cur_pieces[1:]
             else:
                _A      =       cur_pieces[0][1:]
          cur_pieces.append(piece[-1]      )
          new_pieces.extend(__lowerCAmelCase      )
       else:
          new_pieces.append(__lowerCAmelCase      )

    return new_pieces

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					Optional[int]      )   ->	Dict:
    return self.sp_model.PieceToId(__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					Optional[int]      )   ->	List[Any]:
    return self.sp_model.IdToPiece(__lowerCAmelCase      )

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					Optional[Any]      )   ->	Optional[Any]:
    _A      =       []
    _A      =       ''''''
    _A      =       False
    for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
          if not prev_is_special:
             out_string += " "
          out_string += self.sp_model.decode(__lowerCAmelCase      ) + token
          _A      =       True
          _A      =       []
       else:
          current_sub_tokens.append(__lowerCAmelCase      )
          _A      =       False
    out_string += self.sp_model.decode(__lowerCAmelCase      )
    return out_string.strip()

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = None      )   ->	List[int]:
    _A      =       [self.sep_token_id]
    _A      =       [self.cls_token_id]
    if token_ids_a is None:
       return cls + token_ids_a + sep
    return cls + token_ids_a + sep + token_ids_a + sep

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = None			,						__lowerCAmelCase					:					bool = False      )   ->	List[int]:

    if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=__lowerCAmelCase			,						token_ids_a=__lowerCAmelCase			,						already_has_special_tokens=__lowerCAmelCase      )

    if token_ids_a is not None:
       return [1] + ([0] * len(__lowerCAmelCase      )) + [1] + ([0] * len(__lowerCAmelCase      )) + [1]
    return [1] + ([0] * len(__lowerCAmelCase      )) + [1]

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = None      )   ->	List[int]:
    _A      =       [self.sep_token_id]
    _A      =       [self.cls_token_id]

    if token_ids_a is None:
       return len(cls + token_ids_a + sep      ) * [0]
    return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]

 def     snake_case_      (    self					:					Tuple			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Optional[str] = None      )   ->	Tuple[str]:
    if not os.path.isdir(__lowerCAmelCase      ):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
       return
    _A      =       os.path.join(
        __lowerCAmelCase			,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

    if os.path.abspath(self.vocab_file      ) != os.path.abspath(__lowerCAmelCase      ) and os.path.isfile(self.vocab_file      ):
       copyfile(self.vocab_file			,						__lowerCAmelCase      )
    elif not os.path.isfile(self.vocab_file      ):
       with open(__lowerCAmelCase			,						'''wb'''      ) as fi:
          _A      =       self.sp_model.serialized_model_proto()
          fi.write(__lowerCAmelCase      )

    return (out_vocab_file,)










'''simple docstring'''






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase									=   logging.get_logger(__name__)

lowerCAmelCase									=   {
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class 		lowerCamelCase	(   _A							):
	snake_case_   = "sew-d"



	def __init__( self		,   a_=32		,   a_=768		,   a_=12		,   a_=12		,   a_=3_072		,   a_=2		,   a_=512		,   a_=256		,   a_=True		,   a_=True		,   a_=("p2c", "c2p")		,   a_="layer_norm"		,   a_="gelu_python"		,   a_=0.1		,   a_=0.1		,   a_=0.1		,   a_=0.0		,   a_=0.1		,   a_=0.02		,   a_=1e-7		,   a_=1e-5		,   a_="group"		,   a_="gelu"		,   a_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)		,   a_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,   a_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,   a_=False		,   a_=128		,   a_=16		,   a_=True		,   a_=0.05		,   a_=10		,   a_=2		,   a_=0.0		,   a_=10		,   a_=0		,   a_="mean"		,   a_=False		,   a_=False		,   a_=256		,   a_=0		,   a_=1		,   a_=2		,   **a_		,   ):
			super().__init__(**a_		,   pad_token_id=a_		,   bos_token_id=a_		,   eos_token_id=a_						)
			lowerCAmelCase  :     Optional[int]       						= hidden_size
			lowerCAmelCase  :     Optional[int]       						= feat_extract_norm
			lowerCAmelCase  :     str       						= feat_extract_activation
			lowerCAmelCase  :     Dict       						= list(a_						)
			lowerCAmelCase  :     int       						= list(a_						)
			lowerCAmelCase  :     Any       						= list(a_						)
			lowerCAmelCase  :     Optional[Any]       						= conv_bias
			lowerCAmelCase  :     Tuple       						= num_conv_pos_embeddings
			lowerCAmelCase  :     int       						= num_conv_pos_embedding_groups
			lowerCAmelCase  :     Optional[int]       						= len(self.conv_dim						)
			lowerCAmelCase  :     Dict       						= num_hidden_layers
			lowerCAmelCase  :     str       						= intermediate_size
			lowerCAmelCase  :     Any       						= squeeze_factor
			lowerCAmelCase  :     Optional[int]       						= max_position_embeddings
			lowerCAmelCase  :     Tuple       						= position_buckets
			lowerCAmelCase  :     Union[str, Any]       						= share_att_key
			lowerCAmelCase  :     str       						= relative_attention
			lowerCAmelCase  :     List[str]       						= norm_rel_ebd
			lowerCAmelCase  :     int       						= list(a_						)
			lowerCAmelCase  :     str       						= hidden_act
			lowerCAmelCase  :     Dict       						= num_attention_heads
			lowerCAmelCase  :     Optional[Any]       						= hidden_dropout
			lowerCAmelCase  :     int       						= attention_dropout
			lowerCAmelCase  :     Optional[int]       						= activation_dropout
			lowerCAmelCase  :     Any       						= feat_proj_dropout
			lowerCAmelCase  :     Optional[int]       						= final_dropout
			lowerCAmelCase  :     Union[str, Any]       						= layer_norm_eps
			lowerCAmelCase  :     str       						= feature_layer_norm_eps
			lowerCAmelCase  :     Tuple       						= initializer_range
			lowerCAmelCase  :     int       						= vocab_size

			if (
			    (len(self.conv_stride						) != self.num_feat_extract_layers)
			    or (len(self.conv_kernel						) != self.num_feat_extract_layers)
			    or (len(self.conv_dim						) != self.num_feat_extract_layers)
			):
					raise ValueError(
					    "Configuration for convolutional layers is incorrect."
					    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
					    F'''but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)'''
					    F'''= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`.'''						)

			# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
			lowerCAmelCase  :     List[str]       						= apply_spec_augment
			lowerCAmelCase  :     List[Any]       						= mask_time_prob
			lowerCAmelCase  :     Tuple       						= mask_time_length
			lowerCAmelCase  :     List[Any]       						= mask_time_min_masks
			lowerCAmelCase  :     List[str]       						= mask_feature_prob
			lowerCAmelCase  :     Union[str, Any]       						= mask_feature_length
			lowerCAmelCase  :     List[Any]       						= mask_feature_min_masks

			# ctc loss
			lowerCAmelCase  :     Any       						= ctc_loss_reduction
			lowerCAmelCase  :     str       						= ctc_zero_infinity

			# sequence classification
			lowerCAmelCase  :     Dict       						= use_weighted_layer_sum
			lowerCAmelCase  :     Tuple       						= classifier_proj_size





	@property
	def 	_lowerCamelCase     ( self						):
			return functools.reduce(operator.mul		,   self.conv_stride		,   1						)



